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Method for the detection of road bridge pavement crack depth based on acoustic signal analysis
First, the acoustic signal detection environment is preprocessed, and the target for detecting the depth of cracks in road bridge pavement is set. Then, based on this, an interleaved low-rank group convolutional hybrid depth detection structure is established, and the ILGCHDN bidirectional detection model is designed. Finally, the transient acoustic wave method is used to achieve crack depth detection in road bridge pavement. The test results show that compared to the traditional vertical crack depth detection group and the traditional mixed morphology segmentation detection group, the crack depth error mean obtained by the designed acoustic signal crack detection group is relatively small, indicating that the testing method is more reliable and the final results are more accurate, thus having practical application value.
Method for the detection of road bridge pavement crack depth based on acoustic signal analysis
First, the acoustic signal detection environment is preprocessed, and the target for detecting the depth of cracks in road bridge pavement is set. Then, based on this, an interleaved low-rank group convolutional hybrid depth detection structure is established, and the ILGCHDN bidirectional detection model is designed. Finally, the transient acoustic wave method is used to achieve crack depth detection in road bridge pavement. The test results show that compared to the traditional vertical crack depth detection group and the traditional mixed morphology segmentation detection group, the crack depth error mean obtained by the designed acoustic signal crack detection group is relatively small, indicating that the testing method is more reliable and the final results are more accurate, thus having practical application value.
Method for the detection of road bridge pavement crack depth based on acoustic signal analysis
Zhao, Yang (editor) / Liu, Qian (author) / Liu, Zhen (author)
International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024) ; 2024 ; Wuhan, China
Proc. SPIE ; 13170
2024-06-03
Conference paper
Electronic Resource
English
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